Klasifikasi Opini Publik Berbasis Aspek terhadap Affiliate Marketing di TikTok Menggunakan Fine-Tuned IndoBERT
Classification of Public Opinion on Affiliate Marketing on TikTok Based on Specific Aspects Using Fine-Tuned IndoBERT
DOI:
https://doi.org/10.57152/malcom.v6i3.2804Keywords:
Affiliate Marketing, Analisis Sentimen, Aspect-Based Sentiment Classification, IndoBERT, TikTokAbstract
Affiliate marketing pada TikTok berkembang pesat sebagai strategi pemasaran digital berbasis komisi yang melibatkan kreator konten dalam mempromosikan produk kepada pengguna. Aktivitas ini memunculkan berbagai opini publik terkait kualitas produk, kredibilitas kreator, harga dan promosi, pengalaman belanja, serta kualitas konten yang dibagikan melalui media sosial. Penelitian ini bertujuan menganalisis sentimen pengguna terhadap praktik affiliate marketing menggunakan pendekatan aspect-based sentiment classification (ABSC) berbasis Indonesian Bidirectional Encoder Representations from Transformers (IndoBERT). Sebanyak 10.000 komentar TikTok berbahasa Indonesia dikumpulkan dan diproses melalui tahapan preprocessing, deduplikasi, identifikasi aspek menggunakan keyword matching dan Term Frequency — Inverse Document Frequency (TF-IDF) cosine similarity, serta pelabelan sentimen menggunakan pretrained IndoBERT. Kebaruan penelitian ini terletak pada penerapan pendekatan terintegrasi antara identifikasi aspek berbasis keyword matching dan TF-IDF cosine similarity dengan model IndoBERT yang di-fine-tune untuk klasifikasi sentimen berbasis aspek pada komentar TikTok berbahasa Indonesia terkait affiliate marketing. Pendekatan ini mampu memberikan analisis opini yang lebih kontekstual pada domain affiliate marketing yang masih jarang dibahas dalam penelitian sebelumnya. Model kemudian di-fine-tune menggunakan weighted cross-entropy loss untuk menangani ketidakseimbangan kelas pada data. Hasil pengujian menunjukkan akurasi sebesar 89,79%, weighted F1-score sebesar 89,74%, dan macro F1-score sebesar 82,29%. Evaluasi menggunakan stratified 5-fold cross-validation menghasilkan rata-rata akurasi sebesar 91,03%
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